用强化学习动态构建推理树，提升复杂问题解答的准确性和效率。
From Roots to Rewards: Dynamic Tree Reasoning with Reinforcement Learning
- 基于强化学习动态构建推理树，根据实时信心值选择下一步行动。
- 相比静态树结构，推理质量提升12.3%，计算开销减少41%。
- 适合需要高可靠性与灵活性的复杂问答系统开发者使用。
现代语言模型通过思维链（CoT）和检索增强来解决复杂问题，但存在错误传播和知识整合困难。树形推理方法，特别是概率思维树（ProbTree）框架，通过分层结构分解问题，并结合参数化与检索知识进行置信度加权聚合，缓解了这些问题。然而，ProbTree的静态实现存在两大局限：（1）推理树在初始阶段固定，无法根据中间结果动态调整；（2）每个节点需穷举评估所有可能策略，导致计算效率低下。本文提出一种基于强化学习（Sutton and Barto, 2018）的动态框架，将树形推理转化为可适应的过程。该方法根据实时置信度增量构建推理树，并学习最优动作策略（分解、检索或聚合）。在保持ProbTree概率严谨性的同时，通过选择性扩展和资源聚焦，提升了求解质量与计算效率。该工作建立了树形推理的新范式，兼顾概率框架的可靠性与实际问答系统的灵活性。代码已开源：https://github.com/ahmedehabb/From-Roots-to-Rewards-Dynamic-Tree-Reasoning-with-RL。
原文摘要 · Abstract (English)
Modern language models address complex questions through chain-of-thought (CoT) reasoning (Wei et al., 2023) and retrieval augmentation (Lewis et al., 2021), yet struggle with error propagation and knowledge integration. Tree-structured reasoning methods, particularly the Probabilistic Tree-of-Thought (ProbTree)(Cao et al., 2023) framework, mitigate these issues by decomposing questions into hierarchical structures and selecting answers through confidence-weighted aggregation of parametric and retrieved knowledge (Yao et al., 2023). However, ProbTree's static implementation introduces two key limitations: (1) the reasoning tree is fixed during the initial construction phase, preventing dynamic adaptation to intermediate results, and (2) each node requires exhaustive evaluation of all possible solution strategies, creating computational inefficiency. We present a dynamic reinforcement learning (Sutton and Barto, 2018) framework that transforms tree-based reasoning into an adaptive process. Our approach incrementally constructs the reasoning tree based on real-time confidence estimates, while learning optimal policies for action selection (decomposition, retrieval, or aggregation). This maintains ProbTree's probabilistic rigor while improving both solution quality and computational efficiency through selective expansion and focused resource allocation. The work establishes a new paradigm for treestructured reasoning that balances the reliability of probabilistic frameworks with the flexibility required for real-world question answering systems. Code available at: https://github.com/ahmedehabb/From-Roots-to-Rewards-Dynamic-Tree-Reasoning-with-RL
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